Bringing spatial interaction measures into multi-criteria assessment of redistricting plans using interactive web mapping

Abstract

ABSTRACTRedistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties.KEYWORDS: Redistrictingregionalizationmobilityinteractive mapspatial interaction AcknowledgmentsWe would like to thank Gareth Baldrica-Franklin and Professor Robert Roth for their help and guidance in the development of the web map. We would also like to thank Professor Jin-Yi Cai for sharing his expertise on modifying the ensemble distribution in algorithmic design.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe mobility flow dataset used in this research is publicly available on GitHub: https://github.com/GeoDS/COVID19USFlows and from SafeGraph. The other aggregated data that support the findings of this study are available from the U.S. census bureau. Due to the privacy protection policies of the data providers, the voting data used here are not publicly available.Additional informationFundingThis project is supported by the University of Wisconsin 2020 WARF Discovery Initiative funded project: Multidisciplinary Approach for Redistricting Knowledge. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).